Offline interactive natural language processing results

ABSTRACT

Interactive natural language processing (NLP) results may be generated that allow a user to interact with the NLP results but do so in an offline manner so that the documents being processed need not be stored online. To provide interactive NLP results, event handlers may be attached to elements of the NLP results. A user may then select a word or phrase of the NLP results to cause computer software provided with the NLP to present the interactive features. For example, a user may click on a definite noun phrase to view information for diagnosing antecedent basis errors. For another example, a user may click on a word to view information about how that word is used in a document, such as viewing portions of the document that include the word or variants of the word.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/394,445, filed Apr. 25, 2019, and titled “OFFLINE INTERACTIVE NATURALLANGUAGE PROCESSING RESULTS” (GTC Docket No. PBOT-0001-U01).

U.S. patent application Ser. No. 16/394,445 claims the benefit ofpriority to U.S. Provisional Patent App. Ser. No. 62/664,326, filed Apr.30, 2018, and titled “PREDICTING FUTURE PATENT OUTCOMES” (GTC Docket No.PBOT-0001-P01); and U.S. Provisional Patent App. Ser. No. 62/793,245,filed Jan. 16, 2019, and titled “OFFLINE INTERACTIVE NATURAL LANGUAGEPROCESSING RESULTS” (GTC Docket No. PBOT-0001-P02).

The content of each of the foregoing applications is hereby incorporatedby reference in its entirety for all purposes.

BACKGROUND

Natural language processing (NLP) may be used to facilitate the usage,understanding or improvement of documents. For example, NLP tools mayinclude proofreading of documents, automated generation of documents,checking documents for plagiarism, translation of documents, orsummarization of documents. Networked or cloud services may be availableto provide natural language processing of documents, and cloud-based NLPtools may provide convenience or improved performance over NLP toolsthat are installed on end-user devices, such as personal computers. Forexample, cloud-based NLP tools may be faster, more accurate, and may beupdated more frequently.

When processing documents of a more sensitive nature (such asconfidential documents), concerns may be raised over cloud processing ofthe documents, such as the risk of the documents being publiclydisclosed or obtained by third parties. Accordingly, it may desired toprovide techniques for natural language processing of documents thatprovide the convenience or performance of cloud-based tools but alsoreduce risks so that the risks may be more comparable with offlinetools.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 is an example of a system for providing offline, interactive NLPresults using cloud-based NLP tools.

FIG. 2 is an example method for providing offline, interactive NLPresults using cloud-based NLP tools.

FIG. 3 is an example user interface for providing offline, interactiveNLP results using cloud-based NLP tools.

FIGS. 4A-C are example data items that may be used to provide offline,interactive NLP results using cloud-based NLP tools.

FIGS. 5A-C are example user interfaces that provide interactive NLPresults relating to antecedent basis of a patent claim.

FIG. 6 is an example HTML data item for providing interactive NLPresults relating to antecedent basis of a patent claim.

FIG. 7 is an example method for providing interactive NLP resultsrelating to antecedent basis of a patent claim.

FIGS. 8A-B are example user interfaces that provide interactive NLPresults relating to word support of a patent claim.

FIG. 9 is an example HTML data item for providing interactive NLPresults relating to word support of a patent claim.

FIG. 10 is an example method for providing interactive NLP resultsrelating to word support of a patent claim.

FIGS. 11A-B are example user interfaces that provide interactive NLPresults relating to reference labels of a patent application.

FIG. 12 is an example HTML data item for providing interactive NLPresults relating to reference labels of a patent application.

FIG. 13 is an example device for providing interactive NLP results.

FIG. 14 is an example method for assisting a drafter in keeping track ofreference labels in a document.

FIGS. 15A-F are example grant rate timelines.

FIG. 16 is a scatter plot comparing the three-year grant rate with thebackward grant rate.

DETAILED DESCRIPTION

Network or cloud-based natural language processing (NLP) tools areavailable for a wide variety of applications. For example, cloud-basedNLP tools may include proofreading of documents, automated generation ofdocuments, checking documents for plagiarism, translation of documents,or summarization of documents. Companies may provide tools in the formof software as a service (SAAS) to make it easy for end users to performnatural language processing of their documents. In some implementations,a company may store a user's documents to facilitate additionalprocessing of the documents or provide an interactive application wherethe user is able to interact with a first set of results to obtainadditional information about the first set of results or to do anothertype of processing to obtain a second set of results.

For some applications, the documents being processed may be of asensitive nature, such as confidential documents. Users may desire touse the cloud-based tools, but may be cautious of having their documentsstored in the cloud. Accordingly, the techniques described herein areadapted to provide many of the benefits of cloud-based tools, but in amanner that reduces security concerns of users. In some implementations,the cloud services described herein may process a user's document,provide results back to the user in a manner that allows the user tointeract with the results offline, and immediately discard allinformation about the document. For example, the cloud services mayprocess the user's document in the volatile memory of the computerperforming the processing without storing the document or informationabout the contents of the document to a database or other non-volatilestorage.

FIG. 1 illustrates an example system 100 for providing offline,interactive NLP results using cloud-based NLP tools.

In FIG. 1, a user may use user device 110 to submit a document for NLPprocessing. User device 110 may be any device that allows a user tosubmit a document, such as a personal computer or a mobile device (e.g.,a smart phone). The document may be submitted using any appropriatetechniques. For example, a company providing the NLP services mayprovide a web page that allows the user to submit the document. The usermay select the document from storage of user device 110 (or otherstorage location) and submit the form to send the document forprocessing. The user may also copy the contents of the document andpaste them into a form field to submit the contents of the document.

The document may be submitted for processing using network 120. Network120 may include any appropriate communications network, such as theInternet, a mobile device network, a wide area network, or a local areanetwork.

The company may receive the document from the user at server computer130. Server computer 130 may implement any appropriate techniques forassisting the processing of the document, such creating a networkconnection with user device 110, performing load balancing acrossmultiple server computers, and so forth.

Server computer 130 may interact with or access NLP component 140 toperform natural language processing of the document. NLP component 140may perform processing for any desired application of NLP (e.g.,document proofreading). NLP component may provide NLP results that aretransmitted to user device 110 via server computer 130. NLP component140 may include one or both of software executed by server computer 130or a computer different from server computer 130 that executes softwarefor generating the NLP results.

FIG. 2 is a flowchart of an example method for providing offline,interactive NLP results using cloud-based NLP tools. In FIG. 2 and otherflowcharts herein, the ordering of the steps is exemplary, not all stepsare required, steps may be combined or sub-divided, or other steps maybe added. The methods described by any flowcharts described herein maybe implemented, for example, by any of the computers or systemsdescribed herein.

At step 210, a user uploads a document for processing by a company thatprovides NLP processing services. For example, a user may upload adocument using a form of a web page. At step 220, one or more serversmay process the document to generate NLP results. Any appropriate NLPprocessing may be performed, such as proofreading of the document.

The NLP results may be generated in any appropriate format, such as aweb page in hypertext markup language (HTML) or extensible markuplanguage (XML) format. In some implementations, the NLP results mayinclude a first data item that includes HTML, a second data item thatincludes computer-executable software, such as JavaScript, and otherdata items, such as cascading style sheet (CSS) data.

At step 230, the server transmits the NLP results to the user device.For example, the server may transmit one or more data items, such asHTML, JavaScript, CSS, images, or any other appropriate data.

At step 240, the server discards all information about the document andthe NLP results. In some implementations, this step may be optional, andthe server may retain some information about the document for the NLPresults for a limited period of time or for an extended period of time.In some implementations, the document is processed in volatile memory ofthe server and the document and NLP results are not saved tonon-volatile storage. In some implementations, information about thedocument and the NLP results may be present on the server for a veryshort period of time, such as several seconds.

At step 250, the user receives the NLP results and is able to useinteractive features of the NLP results. For example, a browser of theuser's device may render the HTML to present a web page and may executethe JavaScript software to provide interactive features for the user. Insome implementations, all information about the document and NLP resultsis no longer present on the server when the NLP results are presented onthe user device.

The techniques described above in FIGS. 1 and 2 may be used with anytype of document and for any appropriate application of NLP. For clarityof presentation, the techniques described herein will use a patentapplication (or an office action response) as an example of a documentthat may be processed and performing proofreading of the patentapplication as an example application of NLP. The techniques describedherein, however, are not limited, to patent applications or proofreadingdocuments.

FIG. 3 is example user interface for NLP results of proofreading apatent application. The user interface of FIG. 3 may be presented usingany appropriate techniques. For example, the user interface may bepresented by a web browser using one or more data items, such as HTML orJavaScript. This user interface may be presented, for example, after auser has submitted one or more documents of the patent application(e.g., a Microsoft Word file with the text of the patent application ordrawings in a PDF file, a Microsoft PowerPoint file, or a MicrosoftVisio file).

The NLP results of FIG. 3 include information about the patentapplication that was processed, such as the title of the patentapplication and the file names of the documents that were processed.FIG. 3 also includes an “Analyze Again” button that allows the user toconveniently reprocess the patent application. For example, the user mayupdate a Microsoft Word document of the application, and clicking theAnalyze Again button may cause a form to be resubmitted with the updatedWord document and cause updated NLP results to be presented to the user.

The NLP results of FIG. 3 also include multiple tabs with differentaspects of the proofreading of the patent application. In FIG. 3, thecontents of the Overview tab are shown and the contents of the othertabs may be shown by clicking on the tabs. The Overview tab may show aclaim tree of the claims of the patent application so that a user mayeasily see how the claims depend from each other. The Numbering tab mayshow possible numbering errors of the claims of the patent application,such as skipped claim numbers, repeated claim numbers, or claims thatdepend on claims of a different type (e.g., a method claim thatincorrectly depends from a system claim). The Antecedent Basis tab mayshow possible antecedent basis errors in the claims. The Word Supporttab may show the support in the specification for individual words ofthe claims, and the Phrase Support tab may show support in thespecification for phrases (e.g., two or more words) of the claims. TheRef Labels tab may show information about reference labels that wereused inconsistently within the specification or inconsistently betweenthe specification and the drawings. The Fig Numbers tab may showinformation about inconsistent use of figure numbers within thespecification or inconsistent use between the specification and thedrawings. The Spec tab may include the text of paragraphs of thespecification.

FIGS. 4A-C are example data items that may be used to provide offline,interactive NLP results using cloud-based NLP tools, such as the userinterface of FIG. 3. FIG. 4A is an example of HTML data, FIG. 4B is anexample of CSS data, and FIG. 4C is an example of JavaScript data. A webbrowser may process these data items to present a user interface usingtechniques known to one of skill in the art.

FIGS. 5A-C are example user interfaces that provide interactive NLPresults relating to antecedent basis of a patent claim and that may bepresented in the Antecedent Basis tab of FIG. 3.

FIG. 5A presents an example patent claim. In this example, portions ofthe claim are annotated to help the user understand possible antecedentbasis errors. In a patent claim, the first time a noun phrase ispresented (e.g., “banana”), the noun phase should generally be presentedas an indefinite noun phrase (e.g., “a banana”) with an indefinitearticle or no article. When the same noun phrase is repeated later inthe claims, the noun phrase should generally be presented as a definitenoun phrase (e.g., “the banana”) with the definite article “the” (orsometimes “said” is used in place of “the”).

In FIG. 5A, the definite noun phrases may be annotated to indicatewhether a definite noun phrase has an antecedent basis. In this example,the definite noun phrase are annotated with highlighting (indicated as abox), and the color of the highlighting indicates whether an antecedentbasis is present. For the phrase “the store”, there is no previousinstance of “store” in the claim so there is no antecedent basis for thephrase. Accordingly, “the store” may be highlighted in red to indicatethat there is not an antecedent basis. For the phrase “the banana”,there is a previous instance of “a banana” so there is an antecedentbasis for the phrase. Accordingly, “the banana” may by highlighted ingreen to indicate that there is an antecedent basis.

In some instances, there may be a partial antecedent basis. For example,the phrase “the peeled banana” is referring to “the banana” that waspeeled in the previous step. The phrase “the peeled banana” does nothave an exact antecedent basis because there is no previous instance of“peeled banana” in the claim. Because there is a previous instance of “abanana” in the claim, however, there is a partial antecedent basis. Insome instances, a partial antecedent basis may be an error and in someinstances it may not be an error. For the example of “the peeledbanana”, most patent practitioners would likely deem it to not be anerror because the meaning of the claim is clear. Because of theuncertainty of whether definite noun phrases with a partial antecedentbasis are errors, they may be annotated differently. Accordingly, thephrase “the peeled banana” may be highlighted in yellow so that a usermay determine whether or not an error is present.

Another type of error that may appear in claims is when an indefinitenoun phrase is used more than once. In the example of FIG. 5A, theindefinite noun phrase “a banana” is used more than once. In thisexample, the second instance of “a banana” is an error and should bereplaced with “the banana”. To indicate possible errors where anindefinite noun phrase is used more than once, the repeated uses ofindefinite noun phrases may be annotated. In the example of FIG. 5A, thesecond instance of “a banana” is annotated with a dashed underline.

To further assist a user in understanding and diagnosing antecedentbasis errors, the first instance of each indefinite noun phrase may alsobe annotated. In the example of FIG. 5A, the first instance of eachindefinite noun phrase is underlined.

In some implementations, the user interface may allow a user to removesome of the annotations. For example, the user interface may includecheck boxes to allow a user hide all annotations for definite nounphrases with an antecedent basis (e.g., hide green highlighting). Forexample, the user interface may allow any of the above annotations to behidden.

The user interface of FIG. 5A allows a user to see the NLP results forthe antecedent basis analysis, but additional techniques may be used tofurther assist the user in understanding and diagnosing the indicatedantecedent basis errors. In some implementations, the NLP results mayinclude executable software to allow the user to select an antecedentbasis error to obtain additional information for understanding anddiagnosing the error.

In FIG. 5B, a user has selected the phrase “the peeled banana” (e.g., byclicking on it). The executable software detects that that phrase wasclicked, and in response, annotates other words of the claim to assistthe user. In particular, the software may extract the words of theselected phrase (“peeled” and “banana”) and highlight other instances ofthose words in the claim. For example, in FIG. 5B, the instances of theword “banana” are presented with a bold font weight. In addition, thesoftware may annotate word variants of the words of the selected phrase.For example, the word “peeled” does not appear elsewhere in the claim,but the variant “peeling” does appear in the claim. Accordingly, theword “peeling” may also be annotated and is presented in bold in FIG.5B.

The interactive nature of the NLP results for antecedent basis makes iteasier for the user to understand the indicated antecedent basis errors.In FIG. 5B, the user can quickly see that that claim has the indefinitephrase “a banana” to provide a partial antecedent basis and that theclaim has the verb “peeling”. Accordingly, the user may determine that“the peeled banana” is not an antecedent basis error.

For another example, the user may select the phrase “the store”. Becausethe claim does not contain any other instances of the word “store” (orvariants of the word “store”), no other words of the claim would beannotated. The user can then quickly see that “the store” is anantecedent basis error that needs to be fixed.

In FIG. 5C, the user has selected the second instance of the indefinitenoun phrase “a banana” that is indicated as an error. In response, thesoftware can annotate other instances of the indefinite phrase to helpthe user diagnose the error. In the example of FIG. 5C, that otherinstance of “a banana” is annotated in bold. The user can thus quicklyunderstand the error and correct it.

FIG. 6 presents an example portion of an HTML data item that may be usedto present the NLP results of FIGS. 5A-C. FIG. 6 uses multiple HTMLelements to present the words of a claim. An HTML element includes anopening tag (e.g., <span>), a closing tag (e.g., </span>), and the textbetween the opening and closing tags (including any subelements). HTMLelements may also be a single tag (e.g., <br> or <br/>) but such HTMLelements are not shown in FIG. 6.

The HTML elements may facilitate the presentation of the NLP results ofFIGS. 5A-C. For example, each definite noun phrase with an antecedentbasis may be enclosed in an HTML element that starts with <spanclass=“ab-good”> and ends with </span>; each definite noun phrase with apartial antecedent basis may be enclosed in an HTML element that startswith <span class=“ab-warning”> and ends with </span>; and each definitenoun phrase without an antecedent basis may be enclosed in an HTMLelement that starts with <span class=“ab-error”> and ends with </span>.The class of each of these elements may allow the presentation of thedefinite noun phrases to be annotated, such as by using the coloredhighlighting indicated above.

HTML elements may also be used for the presentation of indefinite nounphrases. For example, the first instance of each indefinite noun phrasemay be enclosed in an HTML element that starts with <spanclass=“indef-np”> and ends with </span>; and subsequent instances ofnoun phrases may be enclosed in an HTML element that starts with <spanclass=“red-under”> and ends with </span>. The class of each of theseelements may allow the presentation of the indefinite noun phrases to beannotated as indicated above.

HTML elements may also be used to facilitate the interactive features ofthe user interface of FIGS. 5A-C. In some implementations, HTML elementsmay include information about word variants by enclosing an individualword with an HTML element that includes metadata that indicates anormalized form or a base form of the word. For example, FIG. 6 includesthe element ‘<span data-lemma=“eat”> eating</span>’ for the word“eating”. The base form of “eating” in this example is “eat” and isincluded in a data attribute of the HTML element. Similarly, other wordsin the claim, both within and outside of noun phrases may be enclosed inHTML elements to indicate the base form of the words.

In some implementations, the following JavaScript and jQuery softwaremay be used to detect when noun phrases are selected and then bold otherwords in the claim that are similar to the words of the selected nounphrase:

$( ′ .ab-warning, .ab-error ′ ).click(bold_noun_phrase_words); $( ′.red-under ′ ).click(bold_indef_noun_phrases); functionbold_noun_phrase_words( ) {  var noun_phrase = $(this); noun_phrase.find( ′ span′ ).each(function( ) {   var lemma =$(this).data( ′ lemma ′ );   $( ′ #ab ′ ).find( ′ [data-lemma =″ ′ +lemma + ′ ″] ′ ).each(function( ) {    $(this).css( ′ font-weight ′ ,900);   });  }); } function normalize_text(text) {  text =text.toLowerCase( ).replace(/ \s+/g, ′ ′ )   .replace(/{circumflex over( )}\s+ | \s+$/g, ′ ′ );  if (text.startsWith( ′a ′ )) {   text =text.slice(2);  } else if (text.startsWith( ′an ′ )) {  text =text.slice(3);   }  return text; } function bold_indef_noun_phrases( ) { var noun_phrase = $(this);  var text1 = normalize_text($(this).text());  $( ′ #ab ′ ).find( ′span.indef-np′ ).each(function( ) {   var text2= normalize_text($(this).text( ));   if (text1 == text2) {   $(this).css( ′ font-weight ′ , 900);   }  }); }

FIG. 7 is a flowchart of an example method for providing interactive NLPresults relating to antecedent basis of a patent claim.

At step 710, an event handler is bound to a first HTML element of afirst definite noun phrase of the claim. For example, a handler may beattached to the first HTML element using the software presented above.Any appropriate handler may be attached, such as a handler for a click,a double-click, or a mouse pointer moving onto the HTML element. In someimplementations, handlers may be attached to HTML elements of alldefinite noun phrases. In some implementations, handlers may be attachedto HTML elements of all definite noun phrases without an antecedentbasis or with a partial antecedent basis, but may not be attached fordefinite noun phrases with an antecedent basis (since there may not be aneed to further diagnose definite noun phrases with an antecedentbasis).

The event handler may be attached using metadata of the first HTMLelement of the first definite noun phrase. The first HTML element mayinclude metadata, such as in attributes of the first HTML element. Anyappropriate attributes may be used to store the metadata, such as an id,a class, or a data attribute. For example, all HTML elements of definitenoun phrases may include a class attribute of “definite-noun-phrase”.For another example, definite noun phrases without an antecedent basismay have a class attribute of “ab-error” and definite noun phrases witha partial antecedent basis may have a class attribute of “ab-warning”.

At step 720, a user-interface event is received that indicates that auser selected the first HTML element. Any appropriate event may bereceived, such as any representation of an HTML event (e.g., that anelement was clicked). The event may be received, for example, as aJavaScript event object or a jQuery event object. The event maycorrespond to any appropriate action of the user, such as the userclicking, double clicking, or moving a mouse pointer over the definitenoun phrase.

At step 730, the first HTML element is obtained using information fromthe received event. For example, the first HTML element may be obtainedusing the software presented above.

At step 740, the first HTML element is processed to obtain informationabout words of the first definite noun phrase. In some implementations,the information about the words of the noun phrase may be the text ofwords, and the text of words may be obtained from the HTML element(e.g., for an HTML element ‘<span class=“ab-error”>the store</span>’).In some implementations, the information about words of the firstdefinite noun phrase may include a base form for some or all of thewords of the first definite noun phrase. For example, the base form of aword may be a lemma, a stem, or a prefix.

In some implementations, information about the base form of words may bestored in metadata of the first HTML element (e.g., in attributes of thefirst HTML element). In some implementations, information about the baseform of the words may be stored in other elements that are enclosed bythe first HTML element, such as presented in FIG. 6. For example, theHTML element for “the store” may be presented as ‘<spanclass=“ab-error”> the <span data-lemma=“store”>store</span></span>’.

Any appropriate techniques may be used to obtain the information aboutwords of the first definite phrase, such as using the software presentedabove. In some implementations, information may be obtained for eachword of the first definite noun phrase and, in some implementations,information may be obtained for some but not all of the words of thefirst definite noun phrase.

At step 750, one or more words from the patent claim (or from adifferent patent claim, such as a parent claim) are identified thatmatch a word of the first definite noun phrase. The matching words maybe within or outside of the first HTML element. In some implementations,a word may be a match if the text of the word is equal to text of a wordwithin the first definite noun phrase. In some implementations, a wordmay be a match if the base form of the word is equal to a base form of aword of the first definite noun phrase (e.g., “peeling” and “peeled”).Words outside of the first HTML element may also be stored in HTMLelements that include metadata with the base form of the word aspresented in FIG. 6. Any appropriate techniques may be used to identifymatching words, such as using the software presented above.

At step 760, the appearance of the one or more matching words aremodified. Their appearance may be modified in any appropriate manner tobring the words to the attention of a user. For example, the word may bepresented with a bold font weight. Any appropriate techniques may beused to identify a matching word, such as using the software presentedabove.

The process described above may be repeated for other definite nounphrases. For example, the user may select a second definite noun phraseof the claim, and then the modifications to the appearance of thematching words of the first definite noun phrase may be cleared, andmatching words for the second noun phrase may be identified and theirappearance modified as described above.

Similar techniques may also be applied for checking subsequent instancesof an indefinite noun phrase as described in FIG. 5C above. For example,the HTML of FIG. 6 and the software presented above may be used toattach handlers to subsequent instances of indefinite noun phrases,receive a user-interface event indicating that a user selected an HTMLelement of a subsequent indefinite noun phrase, obtain the HTML elementof the subsequent indefinite noun phrase, identify other matchingindefinite noun phrases, and modify the appearance of the text of thematching indefinite noun phrases.

NLP results may also be used to proofread other aspects of a patentclaim. FIGS. 8A-B present example user interfaces that provideinteractive NLP results relating to word support of a patent claim andthat may be presented in the Word Support tab of FIG. 3.

FIG. 8A presents the same example patent claim presented above. In FIG.8A, words of the claim are annotated to help the user understand thesupport of individual claim words in the specification. In a patentclaim, it may be desired that words of the claim be used a sufficientnumber of times in the specification so that there is sufficient clarityregarding the meaning of the words in the claim and to ensure that thespecification fully describes the claim.

In FIG. 8A, words are annotated to indicate a count of the number oftimes the word appears in the specification. In some implementations,the count may be for exact matches, and in some implementations, thecount may include word variations (e.g., using base forms of words asdescribed above). In this example, the words are annotated withhighlighting (indicated as a box), and the color of the highlightingindicates how much support the word has. For example, words with a countgreater than 5 may be highlighted in green, words with a count from 1 to5 may be highlighted in yellow, and words with a count of 0 may behighlighted in red. In some implementations, some words deemedunimportant (e.g., prepositions) may not be highlighted.

The NLP results may include interactive features to allow a user to viewadditional information for each highlighted word. In someimplementations, a user may select a word (e.g., by clicking it orhovering the mouse pointer over it) and additional information may bepresented about the count of the number of times the word appears in thespecification. For example, a tooltip box may be presented as shown inFIG. 8A. The additional information may include any appropriateinformation, such as the total count for the word or a count for eachvariation of the word that appears in the specification, as shown inFIG. 8A.

In some implementations, the interactive features may allow a user toview how a word in the claim (and/or variants of the word) was used inthe specification. A user may select a word (e.g., by clicking or doubleclicking the word), and paragraphs (or other portions, such as asentence or multiple sentences) from the specification may be presentedto allow the user to quickly see how the word was used in thespecification. For example, FIG. 8B is a dialog box that showsparagraphs in the specification that use the word “banana”. Theinstances of the word banana may be annotated (e.g., with underline orhighlighting) to allow the user to quickly see where the word was usedin the paragraph. In this example, the word banana appears in 7paragraphs and arrows are provided to allow the user to navigate to theother paragraphs that use the word banana.

FIG. 9 presents an example HTML data item for providing interactive NLPresults relating to word support of a patent claim that may be used topresent the NLP results of FIGS. 8A-B. The HTML elements in FIG. 9 mayfacilitate the presentation of the NLP results. For example, each wordwith a first level of support (e.g., a count greater than 5) may beenclosed in an HTML element that starts with <span class=“support-good”>and ends with </span>; each word with a second level of support (e.g., acount from 1 to 5) may be enclosed in an HTML element that starts with<span class=“support-warning”> and ends with </span>; and each word witha third level of support (e.g., a count of 0) may be enclosed in an HTMLelement that starts with <span class=“support-error”> and ends with</span>. The class of each of these elements may allow the presentationof the words to be annotated, such as by using the colored highlightingindicated above.

HTML elements may also be used to provide interactive information aboutthe counts of each word. For example, an HTML element enclosing a wordmay include metadata that includes the information about the counts. Anyappropriate metadata may be used, such as an attribute of the HTMLelement. In the example of FIG. 9, the count information is stored in atitle attribute. For example, the following is the HTML element for aninstance of the word “eating”: <span data-toggle=“tooltip”title=“eating: 10<br>eats: 6<br>eats: 2”class=“support-good”>eating</span>. This HTML element may also be usedto provide interactive information about the paragraphs of thespecification that include the words of the claims. For example, theword variants may be obtained from metadata of the HTML element, and theword variants may be used to obtain paragraphs that include the wordvariants.

In some implementations, the following JavaScript and jQuery softwaremay be used to detect when words are selected and then provideinformation about counts for the word or present paragraphs that includethe word:

$( ′ [data-toggle=″tooltip″ ] ′).tooltip({html: true}); $(′#word-support .support-good′).dblclick(show_word_dlg); $(′#word-support .support-warning′).dblclick(show_word_dlg); functionshow_word_dlg( ) {  var tooltip_title = $(this).data( ′original-title′);  var word_re =/( ?:{circumflex over ( )}|>)(.* ?):/ig;  var m;  varwords = [ ] ;  while (m = word_re.exec(tooltip_title))  words.push(m[1]);  pars_re = new RegExp( ′ \\b( ′ + words.join( ′ | ′) + ′ )\\b′, ′ ig′ );  pars =$( ′#app p′ ).filter(function( ) {  pars_re.lastIndex = 0;   return pars_re.test($(this).text( ));  }); // Code to show dialog  ... }

FIG. 10 is a flowchart of an example method for providing interactiveNLP results relating to word support of a patent claim.

At step 1010, an event handler is bound to a first HTML element of afirst word of the claim. For example, a handler may be attached to thefirst HTML element using the software presented above. Any appropriatehandler may be attached, such as a handler for a click, a double-click,or a mouse pointer moving onto the HTML element. In someimplementations, handlers may be attached to HTML elements of all words.In some implementations, handlers may be attached to HTML elements ofsome but not all words of a claim (e.g., words deemed unimportant orwords without any support and thus for which additional information maynot be available). Any of the techniques described above may be used tobind the event handler.

At step 1020, a user-interface event is received that indicates that auser selected the first HTML element. Any appropriate event may bereceived (e.g., that an element was clicked). The event may be received,for example, as a JavaScript event object or a jQuery event object. Theevent may correspond to any appropriate action of the user, such as theuser clicking, double clicking, or moving a mouse pointer over thedefinite noun phrase.

At step 1030, the first HTML element is obtained using information fromthe received event. For example, the first HTML element may be obtainedusing the software presented above.

At step 1040, the first HTML element is processed to obtain informationabout the first word. In some implementations, the information about thefirst word may be the text of word, and the text of the word may beobtained from the HTML element (e.g., for an HTML element ‘<span>thestore</span>’). In some implementations, the information about the firstword may include a base form of the word or one or more variants of theword.

In some implementations, information about the word may be stored inmetadata of the first HTML element (e.g., in attributes of the firstHTML element). For example, word variants for “eating” may be stored inmetadata as ‘<span title=“eating: 10<br>eats: 6<br>eats:2”>eating</span>’ or a base form of “eating” may be stored as ‘<spandata-lemma=“eat”>eating</span>’. Any appropriate techniques may be usedto obtain the information about the first word, such as using thesoftware presented above.

At step 1050, one or more text portions of a patent applicationspecification that include the first word or a variant of the first wordare identified. In some implementations, where the information about thefirst word includes the base form of the first word, matching words inthe document may be identified as described above, and one or moreportions of the specification may be obtained that include the matchingword (e.g., the paragraph containing the word). In some implementations,where the information about the first word includes variants of thefirst word, the specification may be searched to find the variants ofthe first word. For example, the search may be performed using regularexpressions as shown in the software above.

In some implementations, the search for the first word (and variants)may be performed in the same HTML data item that is presenting the userinterface for viewing the word support of the claim. For example, theuser interface for word support may be presented in the Word Support tabof FIG. 3, and the patent specification may be presented in the Spec tabof FIG. 3. Because the specification is included in the HTML data itemthat is already present on the user's computer, the specification may besearched in an offline manner, such as by using the software presentedabove. In some implementations, the specification may be included in theHTML data item but may not be visible to the user. For example, thespecification may be enclosed in an HTML element that is not displayed.

At step 1060, a first text portion of the one or more text portions ispresented to a user. The first text portion may be presented using anyappropriate techniques. For example, the first text portion may bepresented in a dialog box, such as the dialog box of FIG. 8B. The usermay then use the controls of the dialog box to view other paragraphs ofthe specification that include the first word or variants of the firstword.

The process described above may be repeated for other words of theclaim. For example, the user may dismiss the dialog and then select asecond word of the claim. Text portions of the specification thatinclude the second word or a variant of the second word may beidentified, and a text portion may be presented to the user.

In some implementations, interactive, offline NLP results may beimplemented as described in the following clauses, combinations of thefollowing clauses, or in combination with other techniques describedherein.

Clause 1. A computer-implemented method, comprising: providing firstdata comprising hypertext markup language (HTML), wherein the first datacomprises: text of a first patent claim, a first HTML element thatencloses a first word of the first patent claim, wherein the first HTMLelement includes first metadata indicating a first level of support forthe first word, a second HTML element that encloses a second word of thefirst patent claim, wherein the first HTML element includes secondmetadata indicating a second level of support for the second word; andproviding second data comprising computer-executable instructions that,when executed, cause at least one processor to perform actionscomprising: binding an event handler to the first HTML element,receiving a user-interface event corresponding to a selection of thefirst HTML element, obtaining the first HTML element using theuser-interface event, processing the first HTML element to obtaininformation about the first word, obtaining text of a first portion of adocument using the information about the first word, wherein the text ofthe first portion comprises the first word or a variant of the firstword, and causing the text of the first portion to be displayed to auser.

Clause 2. The computer-implemented method of clause 1, wherein theinformation about the first word comprises a base form of the first wordor one or more variants of the first word.

Clause 3. The computer-implemented method of clause 1, wherein the firstHTML element stores the information about the first word in thirdmetadata.

Clause 4. The computer-implemented method of clause 3, wherein the thirdmetadata comprises a title attribute of the first HTML element.

Clause 5. The computer-implemented method of clause 1, wherein the firstmetadata causes presentation of the first word using a first color andthe second metadata causes presentation of the second word using asecond color, and wherein the first color is different from the secondcolor.

Clause 6. The computer-implemented method of clause 1, wherein theuser-interface event corresponds to a click or double click of the firstHTML element.

Clause 7. The computer-implemented method of clause 1, wherein causingthe text of the first portion to be displayed comprises causing a dialogbox to be displayed.

Clause 8. The computer-implemented method of clause 7, wherein thedialog box includes controls for viewing other paragraphs comprising thefirst word or a variant of the first word.

Clause 9. The computer-implemented method of clause 1, wherein the firstportion is a paragraph of a patent application.

Clause 10. The computer-implemented method of clause 9, whereinobtaining the text of the first portion comprises using a regularexpression.

Clause 11. The computer-implemented method of clause 1, wherein: thefirst data comprises the text of the first patent claim in a first tab;and the text of the first portion in a second tab.

Clause 12. The computer-implemented method of clause 1, wherein thefirst HTML element includes metadata indicating (i) a first variant ofthe first word, (ii) a count for the first variant of the first word,(iii) a second variant of the first word, and (iv) a count for thesecond variant of the first word.

Clause 13. The computer-implemented method of clause 12, wherein thecomputer-executable instructions cause the at least one processor toperform actions comprising: binding a second event handler to the firstHTML element, receiving a second user-interface event corresponding to aselection of the first HTML element and the second event handler,causing count information to be displayed relating to (i) the firstvariant of the first word, (ii) the count for the first variant of thefirst word, (iii) the second variant of the first word, and (iv) thecount for the second variant of the first word.

Clause 14. The computer-implemented method of clause 13, wherein thecount for the first variant of the first word is a number of times thatthe first variant of the first word appears in a specification of apatent application.

Clause 15. The computer-implemented method of clause 14, wherein thesecond user-interface event corresponds to a mouse hovering over thefirst HTML element.

Clause 16. The computer-implemented method of clause 14, wherein causingthe count information to be displayed comprises presenting a tooltip.

Clause 17. A computer-implemented method, comprising: receiving firstdata comprising hypertext markup language (HTML), wherein the first datacomprises: text of a first patent claim, a first HTML element thatencloses a first word of the first patent claim, wherein the first HTMLelement includes first metadata indicating a first level of support forthe first word, a second HTML element that encloses a second word of thefirst patent claim, wherein the first HTML element includes secondmetadata indicating a second level of support for the second word;binding an event handler to the first HTML element; receiving auser-interface event corresponding to a selection of the first HTMLelement; obtaining the first HTML element using the user-interfaceevent; processing the first HTML element to obtain information about thefirst word; obtaining text of a first portion of a document using theinformation about the first word, wherein the text of the first portioncomprises the first word or a variant of the first word; and causing thetext of the first portion to be displayed to a user.

Similar techniques may also be applied for checking the support ofphrases in the claims. An event handler may be bound to an HTML elementthat encloses a phrase of the patent claim, a user-interface event maybe received corresponding to the selection of the HTML element, the HTMLelement may be obtained from the event, the HTML element may beprocessed to obtain information about the phrase (e.g., the text of thephrase), one or more text portions of the specification may be obtainedthat include the phrase, and a text portion may be presented to theuser.

NLP results may also be used to proofread other aspects of a patentapplication. FIGS. 11A-B are example user interfaces that provideinteractive NLP results relating to reference labels of a patentapplication and that may be presented in the Ref Labels tab of FIG. 3.

Patent applications may include reference labels for clarity ofpresentation. A reference label may be assigned, for example, to athing, a part, or a step of a method, and the reference label may beused in the drawings and in the text of the specification.

FIG. 11A is an example user interface for presenting errors that mayoccur with reference labels. In FIG. 11A, the first column shows thereference label, the second column provides information about how thereference label was used in the specification, and the third columnprovides information about how the reference label was used in thedrawings.

For example, reference label 100 was used with the word “banana” 37times, was used with the phrase “peeled bananas” 5 times, was used inone instance without text before it, and appeared in drawing figurenumbers 1, 3, and 4. Reference label 100 may be flagged as a warningsince it was used with more than phrase or because it was used in aninstance without text. Reference label 100 may not be flagged as anerror because “banana” and “peeled bananas” are similar to each other,and such use of the reference label may not be considered inconsistent.

Reference label 110 was used with the word “display” 3 times andappeared in drawing figure number 1. Reference label 110 may be flaggedas no error since it was used with a single phrase and appears in boththe specification and the drawings.

Reference label 120 was used with the phrase “commercial establishment”5 times, was used with the word “banana” once, and appeared in drawingsfigure number 2. Reference label 120 may be flagged as an error since itwas used with phrases that are not similar to each other.

Reference label 130 was used with the word “apple” once and does notappear in the drawings. Reference label 130 may be flagged as an errorsince it does not appear in the drawings.

Reference label 140 was used in the drawings and does not appear in thespecification. Reference label 140 may be flagged as an error since itdoes not appear in the specification.

The cells of the table may be annotated (e.g., using metadata on anelement, such as a class attribute) to facilitate presentation ofwhether there is no error, a warning, or an error. For example, wherethere is an error (e.g., specification column of label 120, drawingscolumn of label 130, and specification column of label 140), thebackground of a cell may displayed as red; where there is a warning(e.g., the specification column of reference label 100), the backgroundof a cell may be displayed as yellow, and where there is no error orwarning, the background of the cell may be presented as green.

The NLP results may also include interactive features to allow a user tobetter understand the errors and warnings. In some implementations, auser may click on the first or second column for a reference label tosee text portions of the specification where the reference label wasused. For example, where a user selects a first column for a referencelabel (e.g., the reference label itself), text portions may be shownwhere the reference label was used. For another example, where a userselects text of the second column of a reference label (e.g., the word“display” for reference label 110), text portions may be shown where thereference label was used with the selected text.

FIG. 11B illustrates an example dialog that may be shown after a userselects the text “display” of reference label 110. The dialog indicatesthat 2 paragraphs include the text “display 100”, presents a firstparaph, and also presents controls to allow the user to see the otherparagraph.

FIG. 12 is an example HTML data item for providing interactive NLPresults relating to reference labels of a patent application and thatmay be used to present the NLP results of FIGS. 11A-B. The HTML elementsin FIG. 12 may facilitate the presentation of the NLP results. Forexample, the HTML element for a cell without an error or warning mayinclude metadata such as ‘class=“rl-good”’; the HTML element for a cellwith a warning may include metadata such as ‘class=“rl-warning”’; andthe HTML element for a cell with an error may include metadata such as‘class=“rl-error”’. The class of each of these elements may allow thepresentation of the reference labels to be annotated, such as by usingthe background colors indicated above.

The reference labels or the phrases of the specification column may alsobe enclosed by an HTML element with metadata to facilitate selection ofthe reference label or phrase for displaying additional information,such as the information presented in the dialog box of FIG. 11B. Forexample, the phrase “peeled bananas” may be enclosed in the followingHTML element: <span data-label=“peeled bananas”>peeled bananas</span>.

In some implementations, the following software may be used to detectwhen a reference label or text of a reference label is selected topresent portions of the specification that use the reference labeland/or phrase:

$( ′td.label-num′ ).dblclick(show_label_dlg); $( ′ td.label-text span′).dblclick(show_label_text_dlg); function show_label_dlg( ) { search_term = $(this).text( );  pars_re = new RegExp( ′ \\b( ′ +search_term + ′ )\\b′, ′ig′ );  pars = $( ′ #app p′ ).filter(function( ){   pars_re.lastIndex = 0;   return pars_re.test($(this).text( ));  }); // Code to show dialog  ... } function show_label_text_dlg( ) {  varref_text = $(this).data( ′ label ′ );  var tr = $(this).parent().parent( );  var td = tr.find( ′ td.label-num′ );  var ref_label =td.text( );  if (ref_text === ′ ′ ) {   search_term = ref_label;  } else{   search_term = ref_text + ′ ′ + ref_label;  }  pars_re = new RegExp(′ \\b( ′ + search_term + ′ )\\b′, ′ig′ );  pars = $( ′ #app p′).filter(function( ) {   pars_re.lastIndex = 0;   returnpars_re.test($(this).text( ));  });  // Code to show dialog  ... }

A method for showing text portions of the specification corresponding toa reference label or a combination of a reference label and a phrase maybe implemented using techniques similar to FIG. 10. A first eventhandler may be bound to an HTML element of a reference label to allow areference label to be selected, a second event handler may be bound toan HTML element for a phrase of a reference label, a user-interfaceevent may be received corresponding to selection of a reference label ora phrase of a reference label, the HTML element corresponding to theevent may be obtained, the HTML element may be processed to obtaininformation for identifying matching text portions of the specification,one or more text portions may be identified in the document (e.g., usingregular expressions), and a first text portion may be presented to theuser.

The process described above may be repeated for other reference labelsand/or text phrases. For example, the user may dismiss the dialog andthen select another reference label or phrase. Text portions of thedocument for the selection may be identified, and a text portion may bepresented to the user.

FIG. 13 illustrates components of one implementation of a computingdevice 1300 for implementing any of the techniques described above. InFIG. 13, the components are shown as being on a single computing device,but the components may be distributed among multiple computing devices,such as a system of computing devices, including, for example, anend-user computing device (e.g., a smart phone or a tablet) and/or aserver computing device (e.g., cloud computing).

Computing device 1300 may include any components typical of a computingdevice, such as volatile or nonvolatile memory 1310, one or moreprocessors 1311, and one or more network interfaces 1312. Computingdevice 1300 may also include any input and output components, such asdisplays, keyboards, and touch screens. Computing device 1300 may alsoinclude a variety of components or modules providing specificfunctionality, and these components or modules may be implemented insoftware, hardware, or a combination thereof. Below, several examples ofcomponents are described for one example implementation, and otherimplementations may include additional components or exclude some of thecomponents described below.

Computing device 1300 may have web server component 1320 that mayperform any appropriate techniques for receiving hypertext transferprotocol requests and providing responses, such as responding to a postof a form of a web page, using any appropriate techniques. Computingdevice 1300 may have document processing component 1321 that may parse asubmitted document (e.g., PDF, Microsoft Word, PowerPoint, or Visio) toobtain text and other information from the document using anyappropriate techniques. Computing device 1300 may have natural languageprocessing component 1322 that may perform any of the NLP tasksdescribed herein using any appropriate techniques. Computing device 1300may have NLP result generation component 1323 that may generateinteractive NLP results using any appropriate techniques, such as byusing any of the techniques described herein. Computing device 1300 mayhave web browser component 1324 that may process data items (e.g., HTMLand JavaScript) to present interactive NLP processing results, such asby using any of the techniques described herein.

Computing device 1300 may include or have access to various data stores.Data stores may use any known storage technology such as files,relational databases, non-relational databases, or any non-transitorycomputer-readable media. Computing device 1300 may have users data store1330 that may be used to store authentication credentials of users toallow them to login and submit NLP processing requests using anyappropriate techniques.

Patent Drafting Assistance Tools

Tools may also be created to assist a person with drafting a patentapplication. The tools described above may also be integrated into aword processing application (e.g., Microsoft Word or Google Docs) andthe proofreading results may be presented alongside the document beingedited, such as in a panel that is presented next to the contents of thedocument being edited. For example, proofreading may be automated suchthat the contents of the document are processed periodically (e.g.,every minute) or after a threshold amount of editing to the document(e.g., a number of characters typed) and the updated proofreadingresults may be presented to the user while the user is editing thedocument.

In some implementations, the proofreading results may be able to changethe appearance of the document being edited or control the wordprocessing software to present a particular portion of the document. Forexample, for the antecedent basis error results of FIGS. 5A-C, a usermay select on a highlighted portion, and executable software running inthe word processing program (e.g., a JavaScript add-in) may cause theword processing program to scroll to present the portion of the claimcorresponding to the highlight selected by the user. For anotherexample, the portion of the claim in the document being edited may beannotated, such as by adding highlighting.

In some implementations, other tools for assisting a patent attorney indrafting patent applications may be provided. For example, a tool may beprovided to assist the patent attorney in keeping track of referencelabels used in a patent application. A patent application may have alarge number of reference labels and the drafter may accidentally usethe wrong reference label or spend time trying to find or remember thereference label that is needed.

As the drafter is writing the patent application, the reference labelsmay be identified. For example, when the drafter enters “the customermay buy banana 100”, the number “100” may be identified as a referencelabel, and the word “banana” may be identified as the phrase thatcorresponds to the reference label.

Any appropriate techniques may be used to identify reference labels. Forexample, reference labels may be presumed to follow a particular format(e.g., at least two digits and optionally followed by one or morecharacters) and the reference labels may be identified using regularexpressions.

In some implementations, text of the document may be processed as it isbeing entered and reference labels identified from the newly enteredtext. In some implementations, the entire document may be scannedperiodically (e.g., every 10 seconds) to identify new reference labelspresent in the document.

Text before an identified reference label may be processed to determinethe phrase that corresponds to the reference label. In someimplementations, NLP techniques may be used to process the text beforethe reference label to identify a noun or noun phrase that precedes thereference label. The NLP techniques may be implemented locally (e.g.,within the word processing application), remotely using cloudprocessing, or a combination of the two.

A data structure may be maintained with the reference labels found inthe document. The data structure may include a field for the referencelabel and a field for the phrases associated with the reference label(there may be more than one since a reference label may appear multipletimes in the document). This information in the data structure may bepresented to the user in the word processing application as a table sothat the drafter has easy access to the reference labels that appear inthe document. For example, the table may use any of the techniquesdescribed above for FIG. 11A and may be color coded to indicate errorsand warnings.

In some instances, a number may be identified as a reference label thatis not actually a reference label. For example, for the text “the personhas age 24”, the number 24 may be identified as a reference label andthe word “age” as the phrase corresponding to the reference label. Thedrafter may desire to remove such mistakes from the table, and a userinterface may be provided to allow the drafter to do so. For example, abutton may be presented for each row, and when the drafter selects abutton, the corresponding reference label may be hidden from view ormoved to the bottom of the table. The data structure of reference labelsmay include a field to indicate that the corresponding number is not areference label so that the number is not later recognized as areference label.

In some implementations, the drafter may edit the phrase correspondingto the reference label. For example, the phrase for reference label 100may be “yellow banana” and the drafter may prefer that the referencelabel be associated with “banana”. The user interface may allow thedrafter to edit the phrase associated with a reference label using anyappropriate techniques.

To identify the noun phrases that comes before reference labels, NLPprocessing could be performed on the entire document. Where cloudprocessing is used to identify reference labels and phrases, additionaltechniques may be used to reduce the amount of needed cloud processing(to increase speed of processing and also reduce costs). To reduce theamount of NLP processing, for each instance of a reference label, a textportion may be extracted from the document where the text portionprecedes the reference label. For example, the text portion may be fromthe beginning of the sentence to the reference label or from a previouspunctuation mark (e.g., comma or semi-colon) to the reference label.Such a text portion would likely include the noun phrase that appearsbefore the reference label without too much additional text. PerformingNLP processing on the text portions instead of the entire document maysignificantly reduce the amount of cloud processing for identifying nounphrases corresponding to reference labels.

The data structure of reference labels may also include the text portionfor each instance of a reference label in the document. As the documentis edited, a text portion corresponding to a reference label may beedited, and the text portion may be updated in the data structure. Wherea text portion is modified, NLP processing may be performed on theupdated text portion to determine if the noun phrase associated with thereference label has changed. Where a text portion has not been changed,NLP processing may not be needed for the text portion.

FIG. 14 is a flowchart of an example method for assisting a drafter inkeeping track of reference labels in a document.

At step 1410, a reference label is identified in a document. Anyappropriate techniques may be used to identify a reference label, suchas using regular expressions. In some implementations, the referencelabel may be identified by processing the entire text of the document inorder and sequentially finding reference labels. In someimplementations, the reference label may be obtained by processing aportion of the document that was recently changed, such as a paragraphthat had text added or removed.

At step 1420, a text portion is obtained for the reference label. Insome implementations, the text portion may be text that occursimmediately before the reference label. For example, the text portionmay include text from the beginning of a paragraph or sentence to thereference label or from a punctuation mark (e.g., a period, comma, orsemi-colon) before the reference label to the reference label.

At step 1430, it is decided whether to perform NLP processing on thetext portion to obtain a phrase that corresponds to the reference label.Any appropriate techniques may be used to determine whether to performNLP processing. In some implementations, a rule-based approach may beused that applies rules an existing reference label data structure. Forexample, one or more of the following rules may be used: (i) if thereference label and text portion are already in the data structure, donot perform NLP processing, (ii) if the reference label is not in thedata structure, perform NLP processing, or (iii) if the text portion isnot in the data structure, perform NLP processing. If it is decided toperform NLP processing, then the method proceeds to step 1440, and if itis decided not to perform NLP processing, then the method proceeds tostep 1450.

At step 1440, a noun phrase is obtained by processing the text portion.In some implementations, a part of speech may be assigned to each wordor token in the text portion. In some implementations, the text portionmay be parsed to assign a dependency label and head for each word ortoken in the text portion. A noun phrase may then be selected that isadjacent to or closest to the reference label (e.g., closest to the endof the text portion). In some implementations, at least a portion of theprocessing of step 1440 may be performed using cloud NLP services. Forexample, an API call may be made to a server computer that includes thetext portion, and the API call may return any of the informationdescribed above (e.g., part of speech, dependency label, noun phrase,etc.).

At step 1450, the reference label data structure is updated using thereference label, text portion, and the noun phrase if it was determinedat step 1440. Any appropriate techniques may be used to update the datastructure. For example, if the reference label is not in the datastructure, then a new entry may be created for the reference label. Ifan entry exists for the reference label, then the text portion and nounphrase may be added to the entry. Any other appropriate information maybe added to the reference label data structure, such as informationindicating a location in the document (e.g., a paragraph or line number)where the reference label appears or a date and time of the update. Insome instances, the data structure may not be updated, such as when step1440 was not performed.

At step 1460, it is determined whether additional portions of thedocument remain to be processed. For example, where the entire documentis being processed, then the method may continue with the text of thedocument after the reference label that was identified at step 1410.Where changes to the document are being processed, then the method maycontinue with the text of the changed portion after the reference labelthat was identified at step 1410. Where additional portions of thedocument remain to be processed, then the method continues to step 1410.Where no more portions remain to be processed, then the method continuesto step 1470.

At step 1470, the reference labels and corresponding phrases arepresented to a user, such as in a panel that is presented next to thetext of the document. The reference labels and corresponding phrases maybe presented using any appropriate techniques, such as the techniquesdescribed herein.

After step 1470, the method may be repeated and start again at step1410. In some implementations, the method may be continuously performedwhile the drafter is editing the document. For example, the method maybe performed at fixed intervals of time or after a threshold number ofchanges to the document.

Other variations of the above are possible. For example, all of thereference labels may be identified at once instead of identifyingreference labels one at a time. For another example, text portions maybe extracted for each reference label before determining whether toperform NLP processing. For another example, the presentation of thereference labels to the user may be updated as each reference label isprocessed (e.g., after step 1450).

Patent Examiner Grant Rate

Patent practitioners (e.g., patent attorneys and patent agents) may usestatistics about patent examiners to better understand their chances ofobtaining an issued patent and also to improve prosecution strategy. Acommonly used statistic is a grant rate or allowance rate of anexaminer. For an examiner's cases over a period of time (or all of theexaminer's cases), a grant rate may be the percentage of disposedapplications (applications that were granted or abandoned) that aregranted. For example, a grant rate may be computed asn_granted/(n_granted+n_abandoned) where n_granted is the number grantedduring the time period and n_abandoned is the number abandoned duringthe time period. This grant rate summarizes what has happened over aprevious time period and will be referred to as a backward grant rate.

Instead of looking backward in time, a grant rate timeline may beconstructed that predicts patent outcomes over time from a startingdate, such as a filing date or the date of a first office action. FIG.15A is an example grant rate timeline for the entire USPTO. In thistimeline, the starting date is the date of the first office action andthe time line continues for 4 years after the date of the first officeaction. The bottom portion 1511 is the percentage of applications thatare granted and this increases from about 0% to about 67% after 4 years.The top portion 1513 is the percentage of applications that areabandoned and this increases from about 0% to about 28% after 4 years.The middle portion 1512 is the percentage of applications that arepending and this decreases from about 100% to about 5% after 4 years.Each point along the timeline provides an estimate of your patent statusat that time. For example, you have a 44% chance of being granted apatent by one year after the first office action, a 61% chance after twoyears, a 66% chance after three years, and so forth.

In some implementations, a grant rate timeline may start with the filingdate of an application instead of the date of the first office action.In this scenario, the grant and abandonment rates would be close to 0%for the first 12-18 months until the examiner issues a first officeaction.

To construct this timeline, take all the patent applications of theUSPTO and shift them in time so that the dates of their first officeactions are the same. Then, for each month afterwards, compute thepercentage of patent applications that were granted, still pending, orabandoned by that month. As time goes on, the percentage of patentapplications that are granted or abandoned generally increases.

Examiners in the USPTO may have very different grant rates. Forcomparison, we present timelines for two examiners with very differentgrant rates. In FIG. 15B, Examiner DT, has a very high grant rate with90%, 97%, and 98% of applications granted at one, two, and three yearsafter the first office action, respectively. By contrast, in FIG. 15C,Examiner SP has a very low grant rate with only 1% of applicationsgranted at three years after the first office action.

To compare patent examiners, it may be easier to use a single numberrather than an entire timeline. One metric for comparing examiners is apoint on the timeline, such as at three years, which may be referred toas a three-year grant rate. A three-year grant rate, for example, mayprovide a balance between providing enough time for meaningfulprosecution and obtaining a relatively near-term measure.

To compare the three-year grant rate with the backwards grant rate, FIG.16 illustrates a scatter plot of examiners with the backward grant rateon the vertical axis and the 3-year grant rate on the horizontal axis(SPEs and examiners with a small number of cases have been excluded).The two grant rates are highly correlated with each other with thebackward grant rate being, on average, a little higher than thethree-year grant rate.

To compare the two grant rates, FIGS. 15D and E show the grant ratetimelines for two examiners with the same backward grant rate but withvery different three-year grant rates. FIG. 15D is the timeline forexaminer DY from FIG. 16, and FIG. 15E is the timeline for examiner VPfrom FIG. 16. Examiner DY and VP each have backward grant rates of about65%, but DY's three-year grant rate is 17% and VP's three-year grantrate is 70%. The timelines explain the difference. While the twoexaminers have similar backward grant rates, their three-year grantrates are very different because it takes years longer to get an issuedpatent with DY than with VP. It seems that examiner DY has manyapplications being prosecuted at even 5-6 years after the first officeaction. DY is clearly a much more difficult examiner than VP and this isreflected in the three-year grant rate. Here, the backward grant rate isnot a good indicator because it assigns these two examiners the samedifficulty level.

For another comparison, FIGS. 15D and F show the grant rate timelinesfor two examiners with the same three-year grant rate but with verydifferent backward grant rates. FIG. 15F is the timeline for examiner BGfrom FIG. 16. For examiners DY and BG, you have about an 18% chance ofgetting an issued patent at three years after the first office action.The difference between DY and BG is the abandonment rate rather than thegrant rate. For some reason, 60% of BG's cases are abandoned at threeyears and only 17% of DY's cases are abandoned at three years. Oneplausible explanation for the vastly different abandonment rates betweenDY and BG may be that DY's cases are much more valuable to theapplicants (the group is “medical and surgical instruments”) than BG'scases (the group is “amusement and education devices”). As a result,DY's applicants are willing to spend much more time and money onprosecution than BG's applicants. It may be more accurate to say that DYand BG have similar difficulty levels. The fact that DY's applicants arewilling to spend more time and money on prosecution should not changethe inherent difficulty level of the examiner. Because the three-yeargrant rate assigns them the same difficulty level (18%) and the backwardgrant rate gives them very different difficulty levels (65% and 20%),the three-year grant rate may be a more accurate indicator in thissituation.

An advantage of the three-year grant rate is that it incorporatesinformation about both the difficulty of the examiner and the length oftime to obtain a patent into a single, easy to understand number. If anexaminer has a three-year grant rate of 18%, then an attorney canexplain to his or her client that they have an 18% chance of getting apatent issued in three years. For some examiners, such as in theexamples above, the three-year grant rate may also provide a moreaccurate depiction of the difficulty of an examiner than the backwardgrant rate.

In addition, as compared with a single grant rate number, the full grantrate timeline provides more information in an easy to digest format. Forsome examiners, the timeline may show that they are difficult before thefirst RCE and much easier afterwards. For other examiners, the timelinemay show that they make a decision early in prosecution and that it ishard to change their minds later. An attorney may be able to leveragethis additional information to improve prosecution strategy, such aswhether to file an RCE or a notice of appeal.

A three-year grant rate is an example of a number that may be computedto measure the difficulty of an examiner. As used herein, a number thatmay be computed to measure the difficulty of an examiner includes anynumber that may be computed using a number of granted patentapplications and a total number of patent applications over a specifiedset of patent applications.

As used herein, a granted patent application may include any status of apatent application that indicates that a patent application has receivedat least some preliminary approval from the patent office. For example,a granted patent application may mean that the patent office has issuedthe patent, that the patent office has provided an issue notificationthat indicates a date when a patent will issue and what the patentnumber will be, or that a patent examiner has issued a notice ofallowance.

The difficulty of a patent examiner may be determined by compilinginformation about patent applications examined by the patent examiner.Information about patent applications examined by the patent examinermay be publicly available, such as downloadable from a website run bythe patent office. A set of patent applications may be specified for thepatent examiner. For example, the set of patent applications may includeall patent applications examined by the examiner or some subset of them,such as all patent applications filed after a specified date. It may bedesired to use a set of relatively recent patent applications to reducecomputational demands and to more accurately describe recent behavior ofan examiner. For example, an examiner who has been at the patent officefor 10 years may be currently more or less difficult than he or she was10 years ago.

To determine a difficulty of a patent examiner, information may becompiled about each patent application in the set of applications atsome time period after a start date, such as three years after the dateof the first office action. Any appropriate start dates and time periodsmay be used.

The determination of a start date of a patent application may depend onthe history of the patent application. For example, for all applicationsthat have received at least one office action rejecting claims of theapplication, the start date may be the date of the earliest officeaction (perhaps excluding restriction requirements and other actionsthat do not evaluate the patentability of claims).

For patent applications that are granted (or allowed, etc.) without everhaving received claim rejections, the start date may be the date of thenotice of allowance. For patent applications that are abandoned withoutever having received claim rejections, any appropriate start date may beused such as the filing date, abandonment date, or any other date fromthe file history. For patent applications that are currently pending(not granted and not abandoned) and have not yet received an officeaction, the application may not receive a start date and may be excludedfrom the analysis.

The difficulty of an examiner may be evaluated by compiling statisticsof the applications examined by the examiner at the time period afterthe start date of each application. Any appropriate techniques may beused to compile statistics of the patent applications. In someimplementations, counters may be used to count the number ofapplications with certain statuses at the time period after the startdate of the application. For example, any of the following counters maybe used.

A counter may represent the number of applications that have reached thetime period after the start date. For example, where the time period isthree years after the start date, and the start date of the patentapplication is the previous month, the patent application has not yetreached the time period after the start date. Where the time period isthree years after the start date, and the start date of the patentapplication is 37 months ago, the patent application may have reachedthe time period after the start date.

In determining whether an application has reached the time period afterthe start date, an end date may be used. A patent application may bedetermined to have reached the time period after the start date if thestart date plus the time period is less than the end date.

The end date may be a current date or a date earlier than the currentdate. For example, the end date may be the first of the month, the endof the previous month, or any other appropriate date. Using an end datethat is not the current date may facilitate record keeping andpresentation of the results. For example, when presenting informationabout the difficulty of examiners, the information may be presented asof the end date. Another way of looking at implementing this counter isthat it may represent the number of applications where the start date isless than the end date minus the time period.

A counter may represent the number of patent applications that (i) havereached the time period after the start date and (ii) have a status ofgranted (or allowed etc.) by the time period after the start date.Another way of looking at this counter is that it may represent thenumber of applications where (i) the start date is less than the enddate minus the time period and (ii) the application was granted by thetime period after the start date.

A counter may represent the number of patent applications that havereached the time period after the start date and have a status ofabandoned by the time period after the start date. Another way oflooking at this counter is that it may represent the number ofapplications where (i) the start date is less than the end date minusthe time period and (ii) the application was abandoned by the timeperiod after the start date.

A counter may represent the number of patent applications that havereached the time period after the start date and have a status ofpending at the time period after the start date. Another way of lookingat this counter is that it may represent the number of applicationswhere (i) the start date is less than the end date minus the time periodand (ii) the application was pending (not granted and not abandoned) atthe time period after the start date.

One or more of the counters may then be used to compute the number formeasuring the difficulty of the patent examiner. For example, the numberfor measuring the difficulty of the patent examiner may be computed asthe percentage of applications that have reached the time period afterthe start date that are granted. In some implementations, the number maybe computed as:

$100 \times \frac{n\_ granted}{n\_ reached}$

where n_granted is the number of patent applications that (i) havereached the time period after the start date and (ii) are granted by thetime period after the start date, and n_reached is the number of patentapplications that have reached the time period after the start date.

In some implementations, the number may be computed as:

$100 \times \frac{n\_ granted}{{n\_ granted} + {n\_ pending} + {n\_ abandoned}}$

where n_pending is the number of patent applications that (i) havereached the time period after the start date and (ii) are pending at thetime period after the start date, and n_abandoned is the number ofpatent applications that (i) have reached the time period after thestart date and (ii) are abandoned by the time period after the startdate

In some implementations, the above computations may be performed formultiple time periods, such as a number of months ranging from 1 monthto 48 months. A timeline may then be created that represents adifficulty of the examiner at each time period, such as the timelinespresented above.

In some implementations, the dates may be rounded off to a month tosimplify the computations. For example, the day of the month may bedropped from each date so that each date is represented as a month and ayear.

In some implementations, the above computations may be performed for agroup of examiners, such as an art unit, group, technical center, or theentire patent office. When performing the above computations for a groupof examiners, the counts may be determined for all examiners of thegroup.

In some implementations, a number representing the difficulty of anexaminer may be compared to a number representing the difficulty of agroup of examiners, such as the art unit of the examiner. Presentingsuch a comparison may assist a patent attorney in determiningprosecution strategy (such as whether to file an appeal or an RCE).

In some implementations, counters may be used to determine a number ofapplications meeting other criteria by the time period after the startdate, such as any of the following counters: (i) a number ofapplications with a notice of appeal in the file history that aregranted, pending, or abandoned by the time period after the start date;(ii) a number of applications with at least one RCE in the file historythat are granted, pending, or abandoned by the time period after thestart date; or (iii) a number of applications with at least oneinterview in the file history that are granted, pending, or abandoned bythe time period after the start date.

Any of the above counters may be used to compute a number for measuringthe difficulty of a patent examiner. In some implementations, examinerstatistics or timelines may be implemented as described in the followingclauses, combinations of the following clauses, or in combination withother techniques described herein.

Clause 1. A method for computing a number for measuring a difficulty ofa patent examiner, the method comprising: selecting a time period forcomputing the number for measuring the difficulty of the patentexaminer; selecting an end date; obtaining information about a pluralityof patent applications examined by the patent examiner; selecting astart date for each patent application of the plurality of patentapplications using the information about the plurality of patentapplications; computing a first number corresponding to a firstplurality of patent applications wherein each patent application of thefirst plurality of patent applications (i) was granted by the timeperiod after the start date of the patent application and (ii) the startdate of the patent application plus the time period is less than the enddate; computing a second number corresponding to a second plurality ofpatent applications wherein, for each patent application of the secondplurality of patent applications, the start date of the patentapplication plus the time period is less than the end date; andcomputing the number for measuring the difficulty of the patent examinerusing the first number and the second number.

Clause 2. The method of clause 1, wherein the time period is threeyears.

Clause 3. The method of clause 1, wherein the end date is a currentdate.

Clause 4. The method of clause 1, wherein the plurality of patentapplications examined by the patent examiner comprise all patentapplications examined by the patent examiner with a filing date after aspecified date.

Clause 5. The method of clause 1, wherein the start date for a patentapplication that has received at least one office action is a date of anearliest office action.

Clause 6. The method of clause 1, wherein the start date for a patentapplication that received a notice of allowance without previouslyreceiving a rejection from the patent examiner is a date of the noticeof allowance.

Clause 7. The method of clause 1, wherein the start date for a patentapplication that was abandoned before receiving an office action is afiling date of the patent application.

Clause 8. The method of clause 1, further comprising: computing a thirdnumber corresponding to a third plurality of patent applications whereineach patent application of the third plurality of patent applications(i) was pending at the time period after the start date of the patentapplication and (ii) the start date of the patent application plus thetime period is less than the end date; computing a fourth numbercorresponding to a fourth plurality of patent applications wherein eachpatent application of the fourth plurality of patent applications (i)was abandoned by the time period after the start date of the patentapplication and (ii) the start date of the patent application plus thetime period is less than the end date.

Clause 9. The method of clause 1, wherein computing the number formeasuring the difficulty of the patent examiner comprises computing apercentage of applications that were granted by the time period afterthe start date of the plurality of patent applications.

Clause 10. The method of clause 1, wherein computing the number formeasuring the difficulty of the patent examiner comprises dividing thefirst number by the second number.

Clause 11. The method of clause 1, further comprising presenting thenumber for measuring the difficulty of the patent examiner to a person.

Clause 12. The method of clause 1, further comprising: computing a grantrate timeline comprising a plurality of grant rates, wherein: each grantrate corresponds to a time period after a start date; and the pluralityof grant rates comprise the number for measuring the difficulty of thepatent examiner.

Clause 13. A system for computing a number for measuring a difficulty ofa patent examiner, the system comprising one or more computersconfigured to implement the method of clause 1.

Clause 14. A device for computing a number for measuring a difficulty ofa patent examiner, the device comprising a processor and a memory, andthe device configured to implement the method of clause 1.

Clause 15. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform the method of clause 1.

Implementation

Depending on the implementation, steps of any of the techniquesdescribed above may be performed in a different sequence, may becombined, may be split into multiple steps, or may not be performed atall. The steps may be performed by a general purpose computer, may beperformed by a computer specialized for a particular application, may beperformed by a single computer or processor, may be performed bymultiple computers or processers, may be performed sequentially, or maybe performed simultaneously.

The techniques described above may be implemented in hardware, insoftware, or a combination of hardware and software. The choice ofimplementing any portion of the above techniques in hardware or softwaremay depend on the requirements of a particular implementation. Asoftware module or program code may reside in volatile memory,non-volatile memory, RAM, flash memory, ROM, EPROM, or any other form ofa non-transitory computer-readable storage medium.

Conditional language used herein, such as, “can,” “could,” “might,”“may,” “e.g.,” is intended to convey that certain implementationsinclude, while other implementations do not include, certain features,elements and/or steps. Thus, such conditional language indicates thatthat features, elements and/or steps are not required for someimplementations. The terms “comprising,” “including,” “having,” and thelike are synonymous, used in an open-ended fashion, and do not excludeadditional elements, features, acts, operations. The term “or” is usedin its inclusive sense (and not in its exclusive sense) so that whenused, for example, to connect a list of elements, the term “or” meansone, some, or all of the elements in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is to be understood to convey thatan item, term, etc. may be either X, Y or Z, or a combination thereof.Thus, such conjunctive language is not intended to imply that certainembodiments require at least one of X, at least one of Y and at leastone of Z to each be present.

While the above detailed description has shown, described and pointedout novel features as applied to various implementations, it can beunderstood that various omissions, substitutions and changes in the formand details of the devices or techniques illustrated may be made withoutdeparting from the spirit of the disclosure. The scope of inventionsdisclosed herein is indicated by the appended claims rather than by theforegoing description. All changes which come within the meaning andrange of equivalency of the claims are to be embraced within theirscope.

What is claimed is:
 1. A computer-implemented method, comprising:providing first data comprising hypertext markup language (HTML),wherein the first data comprises: text of a first patent claim, a firstHTML element that encloses a first word of the first patent claim,wherein the first HTML element includes first metadata indicating afirst level of support for the first word, and a second HTML elementthat encloses a second word of the first patent claim, wherein thesecond HTML element includes second metadata indicating a second levelof support for the second word; and providing second data comprisingcomputer-executable instructions that, when executed, cause at least oneprocessor to perform actions comprising: binding an event handler to thefirst HTML element, receiving a user-interface event corresponding to aselection of the first HTML element, obtaining the first HTML elementusing the user-interface event, processing the first HTML element toobtain information about the first word, obtaining text of a firstportion of a document using the information about the first word,wherein the text of the first portion comprises the first word or avariant of the first word, and causing the text of the first portion tobe displayed to a user.
 2. The computer-implemented method of claim 1,wherein the information about the first word comprises a base form ofthe first word or one or more variants of the first word.
 3. Thecomputer-implemented method of claim 1, wherein the first HTML elementstores the information about the first word in third metadata.
 4. Thecomputer-implemented method of claim 3, wherein the third metadatacomprises a title attribute of the first HTML element.
 5. Thecomputer-implemented method of claim 1, wherein the first metadatacauses presentation of the first word using a first color and the secondmetadata causes presentation of the second word using a second color,and wherein the first color is different from the second color.
 6. Thecomputer-implemented method of claim 1, wherein the user-interface eventcorresponds to a click or double click of the first HTML element.
 7. Thecomputer-implemented method of claim 1, wherein causing the text of thefirst portion to be displayed comprises causing a dialog box to bedisplayed.
 8. The computer-implemented method of claim 7, wherein thedialog box includes controls for viewing other portions comprising thefirst word or a variant of the first word.
 9. A system, comprising atleast one computing device comprising at least one processor and atleast one memory, the at least one computing device configured to:receive first data comprising hypertext markup language (HTML), whereinthe first data comprises: text of a first patent claim, a first HTMLelement that encloses a first word of the first patent claim, whereinthe first HTML element includes first metadata indicating a first levelof support for the first word, and a second HTML element that encloses asecond word of the first patent claim, wherein the second HTML elementincludes second metadata indicating a second level of support for thesecond word; bind an event handler to the first HTML element; receive auser-interface event corresponding to a selection of the first HTMLelement; obtain the first HTML element using the user-interface event;process the first HTML element to obtain information about the firstword; obtain text of a first portion of a document using the informationabout the first word, wherein the text of the first portion comprisesthe first word or a variant of the first word; and cause the text of thefirst portion to be displayed to a user.
 10. The system of claim 9,wherein the first portion is a paragraph of a patent application. 11.The system of claim 10, wherein the at least one computing device isconfigured to obtain the text of the first portion using a regularexpression.
 12. The system of claim 9, wherein the first data comprisesthe text of the first patent claim in a first tab and the text of thefirst portion in a second tab.
 13. The system of claim 9, wherein thefirst HTML element includes metadata indicating (i) a first variant ofthe first word, (ii) a count for the first variant of the first word,(iii) a second variant of the first word, and (iv) a count for thesecond variant of the first word.
 14. The system of claim 13, whereinthe at least one computing device is configured to: bind a second eventhandler to the first HTML element; receive a second user-interface eventcorresponding to a selection of the first HTML element and the secondevent handler; and cause count information to be displayed relating to(i) the first variant of the first word, (ii) the count for the firstvariant of the first word, (iii) the second variant of the first word,and (iv) the count for the second variant of the first word.
 15. Thesystem of claim 14, wherein the count for the first variant of the firstword is a number of times that the first variant of the first wordappears in a specification of a patent application.
 16. The system ofclaim 15, wherein the second user-interface event corresponds to a mousehovering over the first HTML element.
 17. The system of claim 15,wherein the at least one computing device is configured to cause thecount information to be displayed by presenting a tooltip.
 18. One ormore non-transitory computer-readable media comprising computerexecutable instructions that, when executed, cause at least oneprocessor to perform actions comprising: generating first datacomprising hypertext markup language (HTML), wherein the first datacomprises: text of a first patent claim, a first HTML element thatencloses a first word of the first patent claim, wherein the first HTMLelement includes first metadata indicating a first level of support forthe first word, and a second HTML element that encloses a second word ofthe first patent claim, wherein the second HTML element includes secondmetadata indicating a second level of support for the second word; andgenerating second data comprising computer-executable instructions for:binding an event handler to the first HTML element, receiving auser-interface event corresponding to a selection of the first HTMLelement, obtaining the first HTML element using the user-interfaceevent, processing the first HTML element to obtain information about thefirst word, obtaining text of a first portion of a document using theinformation about the first word, wherein the text of the first portioncomprises the first word or a variant of the first word, and causing thetext of the first portion to be displayed to a user.
 19. The one or morenon-transitory computer-readable media of claim 18, wherein theinformation about the first word comprises a base form of the first wordor one or more variants of the first word.
 20. The one or morenon-transitory computer-readable media of claim 18, wherein the firstHTML element stores the information about the first word in thirdmetadata.